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Teaching Statistical Analysis Of Fmri Data

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2003 Annual Conference


Nashville, Tennessee

Publication Date

June 22, 2003

Start Date

June 22, 2003

End Date

June 25, 2003



Conference Session

Teaching Strategies in BME

Page Count


Page Numbers

8.1081.1 - 8.1081.11



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Paper Authors

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Russ Poldrack

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Richard Hoge

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Randy Gollub

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Mark Vangel

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Ian Lai

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Douglas Greve

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Julie Greenberg

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NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

Session 2109


Ian Lai1, Randy Gollub2,3,4, Richard Hoge3, Douglas Greve3, Mark Vangel3, Russ Poldrack5, Julie E. Greenberg4,6 1 Department of Electrical Engineering and Computer Science, MIT 2 Department of Psychiatry, Massachusetts General Hospital 3 MGH/MIT/HMS Martinos Center for Biomedical Imaging 4 Harvard-MIT Division of Health Sciences and Technology 5 Department of Psychology, UCLA 6 Research Laboratory of Electronics, MIT


Functional magnetic resonance imaging (fMRI) represents a new and important topic in biomedical engineering. Statistical analysis of fMRI data is typically performed using free or commercial software packages that do not facilitate learning about the underlying assumptions and analysis methods; these shortcomings can lead to misinterpretation of the fMRI data and spurious results. We are developing an instructional module for learning the fundamentals of statistical analysis of fMRI data. The goal is to provide a tool for learning about the steps and assumptions underlying standard fMRI data analysis so that students and researchers can develop insights required to use existing analysis methods in an informed fashion and adapt them to their own purposes. The module includes a simulation of fMRI data analysis that provides students with opportunities for hands-on exploration of the key concepts using phantom data as well as sample human fMRI data. The simulation allows students to control relevant parameters and observe intermediate results for each step in the analysis stream (spatial smoothing, motion correction, statistical model parameter selection). It is accompanied by a tutorial that directs students as they use the simulation. The tutorial guides students through the individual processing steps, considering multiple cycles of fMRI data analysis and prompting them to make direct comparisons, with emphasis on how processing choices affect the ultimate interpretation of the fMRI data.

I. Introduction

While magnetic resonance imaging (MRI) was introduced for clinical use in the 1970s, functional magnetic resonance imaging (fMRI) was discovered in the early 1990s1-4. This relatively new research tool has found widespread use in a variety of applications at the intersection of biomedical engineering and neuroscience, for example, mapping the boundaries between functional regions of the brain, identifying tumor margins prior to surgery, and investigating the pathology underlying diseases such as schizophrenia. fMRI detects activity in the brain by taking advantage of the change in magnetic properties of the blood surrounding

Proceedings of the 2003 American Society for Engineering Education Annual Conference & Exposition Copyright 2003, American Society for Engineering Education

Poldrack, R., & Hoge, R., & Gollub, R., & Vangel, M., & Lai, I., & Greve, D., & Greenberg, J. (2003, June), Teaching Statistical Analysis Of Fmri Data Paper presented at 2003 Annual Conference, Nashville, Tennessee. 10.18260/1-2--11427

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